Design of an architecture for storing a time connected model of a digital twin
DOI:
https://doi.org/10.34185/1562-9945-4-141-2022-10Keywords:
digital double, multimodal data, digital double temporal model, time series databases, TimescaleDB, architectureAbstract
Recently, in connection with the development of information technology, digital twin technology has become widespread, which allows replacing physical and analog resources with digital ones. The digital twin is also a relevant and effective method of real-time interaction. However, with a wealth of real-time data and information from the digital twin, it is difficult to present them intuitively to users and operators. A digital twin for the study of an arbitrary object can formally be represented as mathematical abstractions designed for efficient processing of temporal multimodal da-ta of the object under study. Based on the above abstractions and their application for the formal specification of the digital twin, the so-called multiimage is built, which ac-tually provides a formal description of the sequences of multimodal data about the ob-ject under study, obtained taking into account time as a result of measurement, genera-tion and other related data acquisition operations. As a result of the study, a sequence of discrete states of the object under study is formed, each of which can be characterized by a set of temporal multimodal data associated with a specific moment in time, which actually serves as a temporal key for a certain state of the object. The purpose of the study is to design the architecture of a software system for stor-ing and then reproducing a temporal connected model using a database designed to store time series data. TimescaleDB, a relational database for time series data, is pro-posed as the main data storage engine. The main challenges for a database that must process a large amount of temporary data is its scalability - to process and store a large amount of data without performance degradation. The proposed method for building a temporal connected model of a digital twin based on TimescaleDB using the concept of hypertables will achieve efficient use of re-sources by parallelizing the load when searching for data, and a three-level architec-ture designed to distribute the responsibilities of a software system can greatly simplify the implementation and subsequent processing of a temporal model of a digital twin.
References
Cimino C., Negri E., Fumagalli L. Review of digital twin applications in manu-facturing //Computers in Industry. – 2019. – V. 113. – P. 103130.
Liu M. et al. Review of digital twin about concepts, technologies, and industrial applications //Journal of Manufacturing Systems. – 2020.
Sulema Ye. S. Methods, models, and means of researched objects digital twins multimodal data processing: The thesis for the degree of Doctor of technical scienc-es, specialty 01.05.03 – Mathematical Support and Software of Computers and Sys-tems / Sulema Yevgeniya Stanislavivna. – Kyiv, 2020. – 343 p.
B. Shah, P. M. Jat, K. Sashidhar. Performance Study of Time Series Databases // Computer Science and Information Technology Trends. – 2022. Crossref. DOI: https://doi.org/10.5121/csit.2022.121406.
J. Mostafa, S. Wehbi, S. Chilingaryan, and А. Kopmann. SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things // Proceedings of the 34th International Conference on Scientific and Statistical Data-base Management (SSDBM '22). Association for Computing Machinery. – New York, 2022. – Article 12. – P. 1–11. DOI: https://doi.org/10.1145/3538712.3538723.
M. Kammakomati. Performance Evaluation of TimescaleDB for Storage of His-torical Data from WinCC OA SCADA Systems // Zenodo. – 2021.
DOI: https://doi.org/10.5281/zenodo.5541641
Shen, L., Lou, Y., Chen, Y., Lu, M., Ye, F.. Meteorological Sensor Data Storage Mechanism Based on TimescaleDB and Kafka // Data Science. ICPCSEE 2019. Com-munications in Computer and Information Science. – Singapore, Springer, 2019. – Vol. 1058 – P. 137-147. DOI: https://doi.org/10.1007/978-981-15-0118-0_11
Freedman M. TimescaleDB vs. InfluxDB: Purpose Built Differently for Time-Series Data [Електронний ресурс] / M. Freedman, A. Sewrathan. – 2020. – Режим доступу до ресурсу: https://www.timescale.com/blog/timescaledb-vs-influxdb-for-time-series-data-timescale-influx-sql-nosql-36489299877/.
Hypertables and chunks [Електронний ресурс]. – 2021. – Режим доступу до ресурсу:
About distributed hypertables [Електронний ресурс]. – 2021. – Режим доступу до ресурсу:
